混合雲霧架構之行動資訊部署機制

Mobile Information Deployment Mechanism in Hybrid Cloud-Fog Architecture

劉翔豪(Xiang-Hao Liu)

指導教授 ： 林志浩

中原大學/商學院/資訊管理研究所/碩士(2020年)

https://doi.org/10.6840/cycu202000772

摘要

霧運算（Fog Computing）是由思科（Cisco）在2014年所創造出來的概念，為雲運算（Cloud Computing）的延伸。此技術採用分散式運算，將運算、通訊、控制和儲存資源與服務分佈給用戶或靠近用戶的設備與系統。對於日益增長的行動資訊，我們採用以資訊為中心的網路（Information-Centric Networks）的方法，考量資訊抵達速率對部署機制的影響。我們透過真實網路模型將問題進行模擬與分析，接下來通過IBM數學解題工具CPLEX進行運算求解，得到部署最佳化的成果。
本研究所探討的問題是屬在條件限制下之行動資訊部署最佳化問題。此問題屬於混合整數二次約束規劃問題（non-linear non-convex mixed integer programming problem），對於求解研究問題，我們透過設計不同的實驗環境，將求解問題的複雜度降低。最後透過實驗方式導入合理化的數據，測試並驗證這些解題程式之效果及效率，以求達到數學最佳化（Mathematical Optimization）。
本研究實驗利用Python語法進行模擬一般節點轉換為ICN網路情況，觀察整體網路延遲狀態，並比較轉換是否有較優良之結果。

關鍵字

霧運算 ； 雲運算 ； 以資訊為中心的網路 ； 延遲

並列摘要

Fog Computing is a concept created by Cisco in 2014 and is an extension of Cloud Computing. This technology uses decentralized operations to distribute computing, communication, control, and storage resources and services to users or devices and systems close to users. For growing operational information, we use Information-Centric Networks to consider the impact of information arrival rates on deployment mechanisms. We simulate and analyze the problem through a real network model, and then use the IBM mathematical problem-solving tool CPLEX to solve the problem and get the best result of the deployment.
The issue explored in this study is the optimization of the deployment of operational information under conditions. This problem belongs to the non-linear non-convex mixed integer programming problem. For solving the research problem, we reduce the complexity of solving the problem by designing different experimental environments. Finally, rationalized data are imported experimentally to test and verify the effectiveness and efficiency of these problem-solving programs in order to achieve Mathematical Optimization.
This research experiment uses Python syntax to simulate the conversion of general nodes to ICN networks, observe the overall network delay state, and compare whether the conversion has better results.

並列關鍵字

Fog Computing ； Cloud Computing ； Information Centric Network ； Delay

參考文獻

[1] F. Ahmad,and T.N. Vijaykumar, “Joint optimization of idle and cooling power in data centers while maintaining response time,” In ACM Sigplan Notices, Vol. 45, No. 3, pp. 243-256, March 2010.

[2] G. Carofiglio, M. Gallo, L. Muscariello, “Bandwidth and storage sharing performance in information centric networking,” Proceedings of tshe AMC SIGCOMM workshop on Information-centric networking, pp. 26-31, 2011.

[3] S. Roy, R. Bose, and D. Sarddar, “A fog-based dss model for driving rule violation monitoring framework on the internet of things,” International Journal of Advanced Science and Technology, vol. 82, pp. 23-32, 2015.

[4] T.H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, and L. Sun, “Fog computing: Focusing on mobile users at the edge,” arXiv preprint, 2015.

[5] N. Cordeschi, D. Amendola, M. Shojafar, and E. Baccarelli, “Distributed and adaptive resource management in cloud-assisted cognitive radio vehicular networks with hard reliability guarantees,” Vehicular Communications, vol. 2, no. 1, pp. 1-12, 2015.